Credit Portfolio Management

The purpose of the course is to identify the sources and products that have credit risks and credit exposures and to develop methodology and policies to detect and manage properly a portfolio of credit risks. 

Key Learning Outcomes:

• Understand the products, transactions and exposures that have credit risks: loan exposure, contingent or committed risks, issuer risks, settlement risks, counterparty-credit risks
• Identify the key elements of credit risk: expected loss, unexpected loss, probability of default, loss given default, recovery rates and exposure at default
• Evaluate the inter-relationships of credit risks within portfolio exposures and how these can be measured and quantified—including risks related to concentration (by industries or countries), correlation and incremental exposures
• Review how the main drivers of credit risk are modeled and sensitized; review the history and evolution of credit models (structured models, intensity models, reduced form models, e.g.)
• Understand how credit portfolio modeling is used within firm-wide risk management and regulatory and economic capital process
• Review certain commercial or industry-related credit models to quantify credit risks, expected loss, unexpected loss; review the advantages and disadvantages of such models
• Review methods to mitigate and hedge credit risks—including collateral, securitization, asset sales, and credit default swaps
• Review cases and portfolios from various banks, financial institutions

Target Audience:

• Bankers, regulators and analysts involved in credit risk management, portfolio risk management and/or regulatory compliance will benefit from the sessions.  
• The audience may include those seeking insight into the quantification of credit portfolio management, exploration of certain issues and topics (mitigation, correlation, concentration risks) and review of various approaches by banks and credit models

Content:

• Day One
• Credit Risk Overview
• The goal of this section is to present a summary of concepts and forms of credit risk.
• Traditional and current definitions of credit risk: default and credit migration
• Credit risk for different market participants e.g., bank lender, fixed income investors, CDS counterparty, credit insurer
• Categories of credit risk: lending, issuer, contingent, pre-settlement, settlement, country / transfer, other
• Different approaches under Basel Framework, US GAAP, IFRS, internal models and market practices (e.g., ISDA agreements)
• Cases, examples: Credit portfolio exposures at various banks, trends, specific risks
• Portfolio Risk Management
• The goal of this section is to review the various techniques used to manage and measure credit risk within a portfolio and to understand the key drivers of credit risk.
• Risk Management Strategy
• Portfolio management objectives: balancing risk appetite and diversification to maximize risk adjusted returns
• Diversification, concentration, risks, granularity, and correlation concepts
• Contagion risk – lessons learned in mature and emerging markets
• Techniques to spread risk: syndication, sub-participation, whole loan sales, credit derivatives, securitization
• Focus on credit default swaps:
• Basic structure and uses
• Variants: index and basket products
• Using index tranche products to understand default correlation
• Cases, examples:  Banks’ use of CDS to manage risks
• Practical issues and uncertainties to consider when managing credit risk: liquidity, basis and wrong way risk, hedging Credit Valuation Allowance (CVA) on derivatives (CVA requirements and capital charges, e.g.)
• Measuring Portfolio Risk
• Portfolio credit risk vs. single credit risk
• Credit risk loss distributions: quantifying expected and unexpected losses
• Contrasting credit and market risk measurement; review of “Credit Value at Risk”
• Key drivers of credit risk:
• Probability of default: using rating models and rating migration
• Default correlation: importance and issues with estimation
• Loss given default: recognition, calculation issues, historical recovery rates
• Exposure at default: estimation issues for different risk types (loans, commitments, counterparty credit risks, e.g.)
• Challenges in addressing correlation: lessons learned, single-factor models
• Day Two
• Credit Risk Models
• The goal of this section is to review the key types and approaches of credit portfolio models.
• Introduction to Credit Portfolio Models
• Basic statistics for risk management:
• Volatility, correlation, VaR, Monte Carlo simulation
• Use of copula functions to model default correlation
• Alternative modelling approaches
• Default models and mark to market / multi-state models
• Structural and reduced form models
• Conditional and unconditional models
• Widely used models: KMV, Credit Risk+, CreditMetrics, CreditPortfolioView, Algorithmics
• Key features and advantages and disadvantages of each model
• Who uses what model?
• Scenario and Sensitivity Analysis
• Why scenario analysis is necessary and different methodologies
• Role of scenario analysis within the stress testing framework
• Sensitivity of key inputs: probability of default, number of rating scales etc
• Capital Allocation
• Regulatory Framework
• Basel III capital adequacy framework for credit risk: -
• Basel standardized and internal ratings approaches (advanced and foundational approaches): loan exposures, contingent exposures, counterparty credit exposure, securitizations
• Basel credit risk formula and comparison with other models
• Basel treatment of correlation risks
• Review bank cases, examples:  Credit RWA, credit capital, trends, compliance
• Economic Capital:
• Key differences between regulatory (Basel II) and economic capital
• Uses of economic capital and economic value-added concepts in a bank
• Relationship between shareholder, regulatory and economic capital
• Conclusion
• Role of credit portfolio management: advisory, policies, benchmarks, limits, stress testing
• Lessons learned from and impact of the sub-prime and the Global Financial Crisis
• Lessons observed during the pandemic and recession, 2020